Python PyMC跟踪未更改？

完整的笔记本是。问题在于最后的考克斯模型。其他人同意该文件

背景。W是一种共同的弱点。我在48个州有430个区。我希望每个状态的值都相同，所以我只需将

W.value

中的48个值切分，以产生430个（共享）缺陷

有人能发现我的错误吗？如果我省略了

W.value[stfips]

部分，则跟踪将更新。例如：

fitted = np.exp(np.dot(X, b1))# + W.value[stfips])

我正在打印

beta.value

，它肯定在变化。我在具有共同弱点的Weibull模型中使用了

W.value[stfips]

符号，它运行良好。考克斯模型是正确的，与此相同，除非我遗漏了一些错误

我使用的是PYMC2.3.2

def cox_spatialshared_model():
    (obs_t, t, pscenter, hhcenter, ncomact, rleader,
    dleader, inter1, inter2, fail, adj) = load_data_cox()

    T = len(t) - 1
    nsubj = len(obs_t)

    stfips = (np.array(
            [41, 41, 41, 41, 41, 41, 41, 45, 45, 45, 45, 35, 35, 35, 35, 
            35, 35, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
            24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
            24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 
            24, 24, 24, 24, 24, 24, 24, 24, 24, 30, 30, 30, 30, 30, 30, 
            18, 18, 18, 18, 18, 18, 27, 47, 47, 47, 47, 47, 47, 47, 47, 
            47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 
            43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 13, 13, 13, 13, 
            13, 8, 8, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,
            26, 26, 26, 26, 26, 26, 26, 21, 21, 21, 21, 21, 21, 21, 21, 
            21, 21, 32, 32, 32, 32, 31, 31, 31, 31, 31, 31, 46, 46, 46, 
            46, 46, 46, 46, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 29, 
            29, 29, 29, 29, 29, 29, 29, 3, 3 , 48, 48, 48, 48, 48, 48, 
            48, 48, 48, 48, 48, 48, 48, 48, 48, 10, 10, 10, 10, 10, 10, 
            10, 10, 34, 34, 34, 34, 34, 34, 34, 34, 34, 42, 42, 42, 42, 
            42, 2, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 4, 15, 
            15, 15, 12, 12, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 
            20, 20, 40, 40, 40, 22, 22, 16, 16, 16, 16, 16, 16, 16, 16, 
            16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 
            16, 16, 16, 16, 16, 16, 16, 16, 25, 25, 25, 25, 25, 25, 25, 
            25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 36, 36, 36, 
            36, 36, 36, 11, 11, 11, 11, 11, 17, 17, 17, 17, 17, 17, 17, 
            17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 19, 
            19, 44, 44, 44, 44, 44, 44,  5, 38, 38, 38, 38, 38, 38, 38, 
            38, 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 
            39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 
            39, 39, 23, 23, 23, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 
            33,  9, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 7, 7, 7, 7, 
            28, 28, 28, 6]) - 1).tolist() # zero-based indexing

    # neighbor weights for each state
    ww = np.zeros((48, 48))
    for i, row in enumerate(adj):
        ww[i, row] = 1.
    # make row-stochastic / row sums to 1
    ww /= ww.sum(1)[:,None]

    # risk set equals one if obs_t >= t
    Y = np.array([[int(obs >= time) for time in t] for obs in obs_t])
    # counting process. jump = 1 if obs_t \in [t[j], t[j+1])
    dN = np.array([[Y[i, j]*(t[j + 1] >= obs_t[i]) * fail[i] 
                    for i in range(nsubj)] for j in range(T)])

    c = Gamma('c', .0001, .00001, value=.01)
    r = Gamma('r', .001, .0001, value=.01)
    dL0_star = r*np.array([t[j+1] - t[j] for j in range(len(t) - 1)])
    mu = dL0_star * c # prior mean hazard
    dL0 = Gamma('dL0', mu, c, value=np.ones(len(t)-1))

    beta = Normal('beta', np.zeros(7), np.ones(7)*.00001, 
                value=np.array([-.36, -.26, -.29, -.22, -.61, -9.73, -.23]))

    tau = Gamma('tau', alpha=.01, beta=.01, value=.001)
    sigma = 1./(tau**.5)
    theta = 1./tau

    @stochastic
    def car(t=tau, value=np.zeros(48)):
        # the conditional mean is the average of the neighbors random effects
        mu_ = np.dot(ww, value)
        # scale precision for the number of neighbors
        taux = t*(ww > 0).sum(1)
        return normal_like(value, mu_, taux)

    # zero-centered W
    W = Lambda('W', lambda mu=car : mu - np.mean(mu))

    X = np.column_stack((pscenter, hhcenter, ncomact, rleader, dleader, inter1, inter2))
    @deterministic
    def idt(b1=beta, dl0=dL0):
        print beta.value
        fitted = np.exp(np.dot(X, b1) + W.value[stfips])
        yy = (Y[:,:T] * np.outer(fitted, dl0))
        return np.transpose(yy)

    dn_like = Poisson('dn_like', idt, value=dN, observed=True)

    return locals()

variables = cox_spatialshared_model()
m3 = MCMC(variables, db='pickle', dbname='junk.pkl')
m3.use_step_method(AdaptiveMetropolis, m3.beta)
m3.sample(10)

运行这个程序，我会打印出下面的beta.value

[-0.36 -0.26 -0.29 -0.22 -0.61 -9.73 -0.23]
[-0.4373514  -0.29374146 -0.22849882 -0.26092336 -0.51110185 -9.71171661
 -0.26297276]
[-0.46275125 -0.1619391  -0.25272671 -0.22495446 -0.51698795 -9.93903304
 -0.18825614]
[-0.37534779 -0.23732345 -0.28472978 -0.18660755 -0.62196907 -9.59429074
 -0.13662035]
[-0.43710784 -0.28943668 -0.4024984  -0.21735101 -0.46731924 -9.47100265
 -0.18356244]
[ -0.36404428  -0.33324418  -0.33465088  -0.30341687  -0.64940603
 -10.11364253  -0.23847174]
[-0.43687098 -0.23435138 -0.25747498 -0.29602854 -0.68939179 -9.91790107
 -0.21975309]
[ -0.37618078  -0.25929464  -0.24237221  -0.23415328  -0.63732179
 -10.51364586  -0.26740793]
[-0.36737311 -0.34901568 -0.25329154 -0.17279257 -0.56187575 -9.45392942
 -0.18361278]
[-0.43960484 -0.28093066 -0.22398154 -0.18963571 -0.6667351  -9.45784103
 -0.23457689]
[-0.32375244 -0.32216695 -0.40709281 -0.26659056 -0.58418152 -9.93182569
 -0.19932304]
 [-----------------100%-----------------] 10 of 10 complete in 0.1 sec

要不是

m3.beta.trace()
array([[-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23],
    [-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23],
    [-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23],
    [-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23],
    [-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23],
    [-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23],
    [-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23],
    [-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23],
    [-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23],
    [-0.36, -0.26, -0.29, -0.22, -0.61, -9.73, -0.23]])

为了子孙后代。感谢您指出问题在于我没有将W作为

idt

的参数。此函数应为

@deterministic
def idt(b1=beta, dl0=dL0, W=W):
    print beta.value
    fitted = np.exp(np.dot(X, b1) + W[stfips])
    yy = (Y[:,:T] * np.outer(fitted, dl0))
    return np.transpose(yy)

一切都应该按预期进行